Software traceability is important in attaining effective end-to-end software engineering. Indeed, it is a significant factor in conducting and achieving a wide range of activities and goals, including tool integration, propagation of change, impact of change analysis, task assistance and semi-automation, consistency management, IT governance, and process assessment. Yet the software traceability problem has remained among the most intractable of problems for more than two decades. There are at least two issues that contribute to this intractability. First, people remain largely unmotivated to provide and evolve traceability information, as the stakeholder who knows the information is rarely the one who benefits from it. Second, even if the people were motivated, it is not feasible for humans to define and maintain traceability information throughout the course of evolution. There are too many stakeholders, too many software artifacts, too much evolution, and far too many interrelationships for this task to be addressed entirely by people.
Accordingly, automation has been attempted in the area of software traceability. Numerous researchers have attempted to automate the identification of artifact interrelationships, using a wide variety of techniques. These attempts have all experienced significant problems with scalability, reliability, correctness and usability and, as a result, none of them are currently in use in any real-world applications. Prior techniques have typically demonstrated different cost-benefit tradeoffs, particularly with respect to performance, precision, and recall. None of the techniques used have been considered efficient and reliable enough for use in real software processes. The limited sets of tools that developers have been offered to utilize traceability information have also demonstrated poor functional and usability characteristics. Fully automating the identification and evolution of interrelationships has not been possible, given that some relationship semantics are implicit and depend on human knowledge.
It would be desirable to overcome the limitations in previous approaches.